Nonparametric Inference: Methods and Applications

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1067

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Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to nonparametric statistical methods. It will cover topics concerning both new methodological proposals or theoretical studies about the properties of existing methods, as well as the applications of methods previously proposed in the literature. The applications can concern all empirical sciences, such as biosciences, chemistry, medicine, engineering, computer science, psychology, economics, business, sociology, and many others.

Authors are encouraged to submit manuscripts that concern theoretical or applied works, provided that they deal with methods of estimation, hypothesis testing, classification, or forecasting that follow a distribution-free approach. The following is a non exhaustive list of such methods:

  • Rank tests;
  • Permutation tests;
  • Bootstraps;
  • Jacknifes;
  • Nonparametric estimations;
  • Resampling methods;
  • Lack-of-fit tests;
  • Nonparametric models;
  • Multivariate analyses;
  • Dimension reduction and variable selection;
  • Stochastic processes;
  • Sample surveys;
  • Time series analyses;
  • Longitudinal and functional data analyses;
  • Nonparametric Bayesian methods;
  • Semiparametric models and methods;
  • Statistical methods for imaging and tomography;
  • Computational statistics;
  • Machine learning.

Dr. Stefano Bonnini
Guest Editor

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Keywords

  • nonparametric statistical methods
  • distribution-free approach
  • hypothesis testing
  • resampling methods
  • machine learning

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Published Papers (1 paper)

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Research

30 pages, 3622 KB  
Article
Central Limit Theorem of the Recursive Estimate of Density Function Under Randomly Censored Data
by Meraou Mohammed Amine and Rabhi Abbes
Stats 2026, 9(4), 72; https://doi.org/10.3390/stats9040072 - 3 Jul 2026
Viewed by 165
Abstract
Kernel density estimation for right-censored data has been extensively studied in the non-recursive setting, whereas recursive approaches adapted to censoring remain largely unexplored despite their considerable computational advantages in sequential data environments. In this paper, we introduce a recursive kernel density estimator for [...] Read more.
Kernel density estimation for right-censored data has been extensively studied in the non-recursive setting, whereas recursive approaches adapted to censoring remain largely unexplored despite their considerable computational advantages in sequential data environments. In this paper, we introduce a recursive kernel density estimator for independent right-censored observations through a Kaplan-Meier weighting scheme. The proposed estimator can be updated incrementally as new observations become available, avoiding repeated re-computation of the entire estimator and substantially reducing memory and computational requirements. Under mild regularity conditions, we establish the asymptotic normality of the estimator and derive its asymptotic variance, which explicitly reflects the effect of the recursive weighting mechanism and the censoring process. We also construct asymptotic confidence intervals for the underlying density using a plug-in variance estimator. An extensive Monte Carlo study, including Gaussian, exponential, heavy-tailed, multimodal, contaminated, and severely censored scenarios, demonstrates that the proposed estimator achieves estimation accuracy comparable to that of the classical censored Parzen-Rosenblatt estimator while offering substantial computational gains. In particular, the recursive procedure remains stable under high censoring levels and exhibits excellent scalability for large and sequentially collected datasets. The proposed methodology provides an efficient and theoretically justified alternative for nonparametric density estimation under right censoring and is particularly suited to applications involving streaming data, such as survival analysis, reliability engineering, medical monitoring, and online forecasting. Full article
(This article belongs to the Special Issue Nonparametric Inference: Methods and Applications)
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